load libraries
library("tidyverse")
library("plyr")
library("dplyr")
library("ggplot2")
library("RColorBrewer")
library("data.table")
library("stringr")
library("janitor")
library("knitr")
library("kableExtra")
library("plotly")
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
# fig.width = 20,
# fig.asp = 0.6,
# out.width = "100%")
load data
data <- read.csv("/Users/nuriteliash/Documents/GitHub/varroa_Ploidy/data/Ploidy.csv") %>%
dplyr::mutate(Family = as.character(Family)) %>%
dplyr::mutate(stage_mature = case_when(
grepl("adult", Stage) ~ "Mature",
!grepl("adult", Stage) ~ "Imature"))
# order the levels
data$body.part <- factor(data$body.part, level=c("Body", "Anterior", "Posterior", "Legs","Hemolymph","Ovary","Testes"))
data$Stage <- factor(data$Stage, level=c("Larvae", "Protonymph", "Deuteronymph", "adult"))
data$Stage_original <- factor(data$Stage_original, level=c("Mom", "Son", "Mature","Daughter", "Deuteronymph", "Protonymph", "Larvae", "Immature"))
levels(data$Family) <- c(levels(data$Family), 0)
data$Family <- factor(data$Family, level= c("0", "1", "11","27","3","2","4", "5"))
p_family_adults = data %>% dplyr::filter(body.part %in% c("Body", "Ovary","Testes")) %>%
dplyr::filter(Stage == "adult") %>%
mutate_at("Family", ~replace_na(.,"0")) %>%
ggplot(aes(y=Ploidy, x=Sex, fill = Sex, lable = Stage)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_classic() + geom_jitter(width=0.1, size=2) +
facet_wrap(~body.part ) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
panel.border=element_rect(colour="black",size=1, fill = NA))+
ggtitle('Mite ploidy in differnet body parts') + ylim(0, 3)
p_fam_body = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Stage %in% c("Larvae", "Protonymph", "Deuteronymph", "adult")) %>%
ggplot(aes(y=Ploidy, x=Sex, fill = Sex, lable = Stage)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_classic() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Stage, nrow = 1 ) +
ggtitle('Mite Ploidy in whole body, in differnet developmental stage') +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
panel.border=element_rect(colour="black",size=1, fill = NA))+
theme(legend.position='right')+ ylim(0, 3)
p_allFam_body_adult = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Stage =="adult") %>%
# mutate_at("Family", ~replace_na(.,"0")) %>%
na.omit() %>%
ggplot(aes(y=Ploidy, x=Family, fill = Sex, lable = Stage)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
#facet_wrap(~Stage ) +
ggtitle('Mite Ploidy in whole body, per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='right')+ ylim(0, 3)
p_fam_body_1_11_27_3 = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Family %in% c("1", "11","27","3")) %>%
ggplot(aes(y=Ploidy, x=Stage_original, fill = Sex, lable = Stage_original)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Family , nrow = 1) + ggtitle('Mite Ploidy per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='none')+ ylim(0, 3)
p_fam_body_2_4 = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Family %in% c("2","4")) %>%
ggplot(aes(y=Ploidy, x=Stage_original, fill = Sex, lable = Stage_original)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Family ) + ggtitle('Mite Ploidy per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='none')+ ylim(0, 3)
p_fam_body_5 = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Family == "5") %>%
ggplot(aes(y=Ploidy, x=Stage_original, fill = Sex, lable = Stage_original)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Family ) + ggtitle('Mite Ploidy per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='none')+ ylim(0, 3)
all developmental stages, males and females, whole body
all developmental stages, males and females, in different body parts
the Ploidy of the male offspring varied, depends on the family
for some families ( 1, 11, 27 and 3) the females Ploidy was higher
then males.
while females looks diploid, males look haploid.
for other families ( 2 and 4) the females Ploidy was similar to that of the male offspring.
in one family (number 5) the male offspring Ploidy was mixed:
one males look diploid, and the other two look haploid.